An Environmentally Sustainable Closed-Loop Supply Chain Network Design under Uncertainty: Application of Optimization

TL;DR

This paper presents a multi-objective optimization model for designing an environmentally sustainable closed-loop supply chain that minimizes costs and CO2 emissions, using genetic algorithms and epsilon constraint methods.

Contribution

It introduces a novel green supply chain design approach that balances economic and environmental objectives under uncertainty.

Findings

Genetic algorithm outperforms epsilon constraint method in solution quality.

The model effectively balances cost and environmental impact.

Results verify the model's capability to address financial and ecological concerns simultaneously.

Abstract

Newly, the rates of energy and material consumption to augment industrial pro-duction are substantially high, thus the environmentally sustainable industrial de-velopment has emerged as the main issue of either developed or developing coun-tries. A novel approach to supply chain management is proposed to maintain economic growth along with environmentally friendly concerns for the design of the supply chain network. In this paper, a new green supply chain design approach has been suggested to maintain the financial virtue accompanying the environ-mental factors that required to be mitigated the negative effect of rapid industrial development on the environment. This approach has been suggested a multi-objective mathematical model minimizing the total costs and CO2 emissions for establishing an environmentally sustainable closed-loop supply chain. Two opti-mization methods are used namely Epsilon Constraint Method, and Genetic Al-gorithm Optimization Method. The results of the two mentioned methods have been compared and illustrated their effectiveness. The outcome of the analysis is approved to verify the accuracy of the proposed model to deal with financial and environmental issues concurrently.